Analyzing Amazon Sales¶

In [1]:
import pandas as pd
import numpy as np
import matplotlib as mtp
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
%matplotlib inline
In [2]:
from warnings import filterwarnings 
filterwarnings('ignore')

1.Load data from Excel.¶

In [4]:
data1 = pd.read_csv('D:/Project 2024/Project Unifield M/Project 1_Analyzing Amazon Sales/Amazon Sales analysis.csv')

2.Top 3 Rows.¶

In [129]:
data1.head(3)
Out[129]:
Region Country Item Type Sales Channel Order Priority Order Date Month-Year Year Order ID Ship date Month-Year.1 Year.1 Units Sold Unit Price Unit Cost Total Revenue Total Cost Total Profit
0 Australia and Oceania Tuvalu Baby Food Offline H 28-05-2010 May-10 2010 669165933 27-06-2010 Jun-10 2010 9925 255 159 2533654 1582244 951411
1 Central America and the Caribbean Grenada Cereal Online C 22-08-2012 Aug-12 2012 963881480 15-09-2012 Sep-12 2012 2804 206 117 576783 328376 248406
2 Europe Russia Office Supplies Offline L 02-05-2014 May-14 2014 341417157 08-05-2014 May-14 2014 1779 651 525 1158503 933904 224599
In [14]:
#---------------------Order date - ship date day in ship date--------------

4.Last 3 Rows.¶

In [5]:
data1.tail(3)
Out[5]:
Region Country Item Type Sales Channel Order Priority Order Date Month-Year Year Order ID Ship date Month-Year.1 Year.1 Units Sold Unit Price Unit Cost Total Revenue Total Cost Total Profit
97 Sub-Saharan Africa Sierra Leone Vegetables Offline C 01-06-2016 Jun-16 2016 728815257 29-06-2016 Jun-16 2016 1485 154 91 228779 135031 93748
98 North America Mexico Personal Care Offline M 30-07-2015 Jul-15 2015 559427106 08-08-2015 Aug-15 2015 5767 82 57 471337 326816 144521
99 Sub-Saharan Africa Mozambique Household Offline L 10-02-2012 Feb-12 2012 665095412 15-02-2012 Feb-12 2012 5367 668 503 3586605 2697132 889473

5.Viewing rows and columns.¶

In [131]:
data1.shape
Out[131]:
(100, 18)
In [61]:
print(f'Total Of Rows in dataset is-{100}' )
print(f'Total Of Column in dataset is-{18}' )

Total Of Rows in dataset is-100
Total Of Column in dataset is-18

6.Basic infromation about dataset of Coloumns types.¶

In [132]:
data1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 18 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Region          100 non-null    object
 1   Country         100 non-null    object
 2   Item Type       100 non-null    object
 3   Sales Channel   100 non-null    object
 4   Order Priority  100 non-null    object
 5   Order Date      100 non-null    object
 6   Month-Year      100 non-null    object
 7   Year            100 non-null    int64 
 8   Order ID        100 non-null    int64 
 9   Ship date       100 non-null    object
 10  Month-Year.1    100 non-null    object
 11  Year.1          100 non-null    int64 
 12  Units Sold      100 non-null    int64 
 13  Unit Price      100 non-null    int64 
 14  Unit Cost       100 non-null    int64 
 15  Total Revenue   100 non-null    int64 
 16  Total Cost      100 non-null    int64 
 17  Total Profit    100 non-null    int64 
dtypes: int64(9), object(9)
memory usage: 14.2+ KB

7.Change to date format.¶

In [9]:
data1['Order Date']=pd.to_datetime(data1['Order Date'])
In [10]:
data1['Ship date']=pd.to_datetime(data1['Ship date'])

8.Cheacking a missing dataset.¶

In [12]:
data1.isnull().sum()
Out[12]:
Region            0
Country           0
Item Type         0
Sales Channel     0
Order Priority    0
Order Date        0
Month-Year        0
Year              0
Order ID          0
Ship date         0
Month-Year.1      0
Year.1            0
Units Sold        0
Unit Price        0
Unit Cost         0
Total Revenue     0
Total Cost        0
Total Profit      0
dtype: int64

Statical¶

1.Statical information about dataset.¶

In [15]:
data1[['Total Revenue','Units Sold','Total Profit']].describe()
Out[15]:
Total Revenue Total Profit Units Sold
count 1.000000e+02 1.000000e+02 100.000000
mean 1.373488e+06 4.416820e+05 5128.710000
std 1.460029e+06 4.385379e+05 2794.484562
min 4.870000e+03 1.258000e+03 124.000000
25% 2.687210e+05 1.214438e+05 2836.250000
50% 7.523145e+05 2.907680e+05 5382.500000
75% 2.212045e+06 6.358290e+05 7369.000000
max 5.997055e+06 1.719922e+06 9925.000000

2.Total revenues.¶

In [18]:
Revenue=data1['Total Revenue'].sum()
print('Total Revenue=',Revenue)

Total Revenue= 137348766

3.Total profit.¶

In [20]:
Profit=data1['Total Profit'].sum()
print('Total Profit=',Profit)

Total Profit= 44168199

4.Total profit.¶

In [5]:
Cost=data1['Total Cost'].sum()
print('Total Cost=',Cost)

Total Cost= 93180569

5.Region wise Revenue and Profit¶

In [43]:
data1.groupby(by=['Region'])[['Total Revenue','Total Profit']].sum().sort_values(by='Total Profit',ascending=False)
Out[43]:
Total Revenue Total Profit
Region
Sub-Saharan Africa 39672031 12183213
Europe 33368930 11082938
Asia 21347091 6113846
Middle East and North Africa 14052706 5761191
Australia and Oceania 14094265 4722161
Central America and the Caribbean 9170386 2846908
North America 5643357 1457942

6.Top 10 Country Wise Total Revenue and Total Profit high to low.¶

In [56]:
data1.groupby(by=['Country'])[['Units Sold','Total Revenue','Total Profit']].sum().head(10).sort_values(by='Total Profit',ascending=False)
Out[56]:
Units Sold Total Revenue Total Profit
Country
Azerbaijan 9255 4478800 1512927
Brunei 6708 4368317 846885
Angola 4187 2798046 693912
Bulgaria 5660 2779199 626224
Bangladesh 8263 902981 606835
Australia 12995 2489933 576605
Burkina Faso 8082 1245113 510217
Austria 2847 1244708 495008
Belize 5498 600821 403773
Albania 2269 247956 166635

7.Item Type Wise Total Revenue and Total Profit.¶

In [51]:
data1.groupby(by=['Item Type'])[['Units Sold','Total Revenue','Total Profit']].sum().sort_values(by='Total Profit',ascending=False)
Out[51]:
Units Sold Total Revenue Total Profit
Item Type
Cosmetics 83718 36601508 14556048
Household 44727 29889713 7412606
Office Supplies 46967 30585380 5929586
Clothes 71260 7787292 5233333
Baby Food 40545 10350328 3886645
Cereal 25877 5322898 2292443
Vegetables 20051 3089056 1265819
Personal Care 48708 3980905 1220622
Beverages 56708 2690795 888048
Snacks 13637 2080734 751945
Meat 10675 4503676 610610
Fruits 49998 466481 120494

8.Total of Items Types.¶

In [40]:
Count=data1['Item Type'].value_counts()
print('Count of Items Types\n',Count)

Count of Items Types
 Item Type
Clothes            13
Cosmetics          13
Office Supplies    12
Fruits             10
Personal Care      10
Household           9
Beverages           8
Baby Food           7
Cereal              7
Vegetables          6
Snacks              3
Meat                2
Name: count, dtype: int64

9.sales Channel wise Unit sold, Total Revenue and Total Porfit.¶

In [140]:
data1.groupby(by=['Sales Channel'])[['Units Sold','Total Revenue','Total Profit']].sum()
Out[140]:
Units Sold Total Revenue Total Profit
Sales Channel
Offline 276782 79094808 24920727
Online 236089 58253958 19247472

10.Sales channel wise Country Profit.¶

In [212]:
D6=data1.groupby(by=['Sales Channel','Country'])[['Total Revenue','Total Profit']].sum().sort_values(by='Country',ascending=False)
D6
Out[212]:
Total Revenue Total Profit
Sales Channel Country
Online Zambia 623289 225247
United Kingdom 188452 46736
Offline Tuvalu 2533654 951411
Online Turkmenistan 3262562 632513
Offline Turkmenistan 2559474 634746
... ... ...
Austria 1244708 495008
Australia 585795 207450
Online Australia 1904138 369155
Offline Angola 2798046 693912
Online Albania 247956 166635

87 rows × 2 columns

11.Order Priority wise profit.¶

In [50]:
data.groupby(by=['Order Priority'])[['Units Sold','Total Revenue','Total Profit']].sum().sort_values(by='Total Profit',ascending=False)
Out[50]:
Units Sold Total Revenue Total Profit
Order Priority
H 154212 48749543 16891601
L 146876 36628129 10858727
M 94832 33116030 9669543
C 116951 18855064 6748328

12.Year wise Total Revenue and Total profit findout.¶

In [53]:
data1.groupby(by=['Year'])[['Total Revenue','Total Profit']].sum()
Out[53]:
Total Revenue Total Profit
Year
2010 19186023 6629568
2011 11129165 2741008
2012 31898644 9213009
2013 20330447 6715420
2014 16630217 5879462
2015 12427983 3996540
2016 12372866 4903838
2017 13373421 4089354

10.Month-Years wise Total Profit findout.¶

In [142]:
data1.groupby(by=['Month-Year']) ['Total Profit'].sum().head(15)
Out[142]:
Month-Year
Apr-11     693912
Apr-12     971008
Apr-13     632513
Apr-14    1838546
Apr-15     624230
Aug-12     248406
Aug-13      23150
Aug-14     306098
Aug-15       1622
Dec-10     641587
Dec-13      53253
Dec-16    1661390
Feb-10    1424411
Feb-11     127723
Feb-12    1553767
Name: Total Profit, dtype: int64

Visualation¶

1.Correlation between Sold Unit with Total Profit¶

In [129]:
plt.figure(figsize=(5,3))
sns.scatterplot(x=data1['Units Sold'],y=data1['Total Profit'])
Out[129]:
<Axes: xlabel='Units Sold', ylabel='Total Profit'>

2.Correlation between TotalRevenue with Total Profit¶

In [7]:
plt.figure(figsize=(5,3))
sns.scatterplot(x=data1['Total Revenue'],y=data1['Units Sold'])
Out[7]:
<Axes: xlabel='Total Revenue', ylabel='Units Sold'>

2.Correlation between Total Cost with Total Profit¶

In [13]:
plt.figure(figsize=(5,3))
sns.scatterplot(x=data1['Total Cost'],y=data1['Total Profit'])
Out[13]:
<Axes: xlabel='Total Cost', ylabel='Total Profit'>

3.Corralation With Dataset¶

In [13]:
da1=data1.describe()
sns.heatmap(np.round(da1.corr(),2),annot=True)
Out[13]:
<Axes: >
In [ ]:
#Region wise Total of Orders ID ------- Pending
In [75]:
da2=data1.groupby(by=['Region'])['Order ID'].count()
da2
Out[75]:
Region
Asia                                 11
Australia and Oceania                11
Central America and the Caribbean     7
Europe                               22
Middle East and North Africa         10
North America                         3
Sub-Saharan Africa                   36
Name: Order ID, dtype: int64

4.Order ID wise.¶

In [9]:
sns.set(rc={'figure.figsize':(20,5)})
sns.histplot(data1['Order ID'],bins=30,kde=True)
Out[9]:
<Axes: xlabel='Order ID', ylabel='Count'>

5.Total Cost wise.¶

In [14]:
sns.histplot(data1['Total Cost'],bins=30,kde=True)
Out[14]:
<Axes: xlabel='Total Cost', ylabel='Count'>

6.Total Profit Wise.¶

In [15]:
sns.histplot(data1['Total Profit'],bins=30,kde=True)
Out[15]:
<Axes: xlabel='Total Profit', ylabel='Count'>

7.Regin Wise Total Profit high to low.¶

In [67]:
region= data1.groupby(by=['Region'])['Total Profit'].sum().sort_values(ascending=False).reset_index()
plt.figure(figsize=(20,5))
sns.barplot(x='Region',y='Total Profit',data =region,palette='viridis')
plt.yscale('log')
plt.title('Region wise Profit',fontsize=20,color='b')
plt.xlabel('Region',fontsize=10,color='r')
plt.ylabel('Total Profit',fontsize=10,color='r')
plt.show()
In [227]:
sold_Region=data.groupby(['Region','Item Type'],as_index=False)['Total Profit'].sum().sort_values(by='Total Profit',ascending=False)
#sns.set(rc={'figure.figsize':(15,5)})
DF=sns.barplot(x='Region',y='Total Profit',data=sold_Region)
for bars in DF.containers:
     DF.bar_label(bars)

8.Country Wise Total Revenue and Total Profit high to low.¶

In [288]:
df=data1.groupby(by=['Country'])[['Total Revenue','Total Profit']].sum().head(20).sort_values(by='Total Profit',ascending=False)
In [289]:
sns.set(rc={'figure.figsize':(20,5)})
df.plot(kind='bar')
plt.title('Country Wise Total Revenue and Total Profit',fontsize=20,color='b')
plt.xlabel('Region',fontsize=15,color='r')
plt.ylabel('Total Profit',fontsize=15,color='r')
plt.show()

9.Order Priority wise profit Total Profit.¶

In [174]:
plt.figure(figsize=(20,5))
df1=data.groupby(by=['Order Priority'])['Total Profit'].sum()
fig=plt.figure(figsize=(3.5,4))
df1=data.groupby(by=['Order Priority']).size()
explode=(0,0.05,0.04,0)
colors=('g','c','y','b')
df1.plot(kind='pie',explode=explode,colors=colors,autopct='%1.2F%%',startangle=190)
plt.axis('equal')
plt.show()

<Figure size 2000x500 with 0 Axes>

10.Sales channnel wise Profit.¶

In [173]:
Da5=data1.groupby(['Region','Sales Channel'],as_index=False)['Total Profit'].sum().sort_values(by='Total Profit')
sns.set(rc={'figure.figsize':(3,3)})
ax=sns.barplot(x='Sales Channel',y='Total Profit',data = Da5)
for bars in ax.containers:
    ax.bar_label(bars)

11.Item Type and Sales Channel wise profit.¶

In [6]:
da7=px.bar(data1,x='Item Type',y='Total Profit',color='Sales Channel',barmode='group',width=1000,height=500)
da7

12.Region wise Sales channel Profit.¶

In [283]:
D5=data1.groupby(by=['Region','Sales Channel'])[['Total Revenue','Total Profit']].sum().sort_values(by='Region',ascending=False)
D5.plot(kind="bar")
Out[283]:
<Axes: xlabel='Region,Sales Channel'>

13.Total of Items Types.¶

In [69]:
sns.set(rc={'figure.figsize':(20,5)})
Count=data['Item Type'].value_counts()
Count.plot(kind='bar')
plt.title('Total of Items Types',fontsize=20,color='b')
plt.xlabel('Items',fontsize=15,color='r')
plt.ylabel('Total',fontsize=15,color='r')
plt.show()

14.Item Type wise Cost and Porfit.¶

In [290]:
DF3=data.groupby(by=['Item Type'])[['Total Cost','Total Profit']].sum()
In [292]:
sns.set(rc={'figure.figsize':(20,5)})
DF3.plot(kind="bar")
plt.title('Items wise Total Cost and Total profit',fontsize=20,color='b')
plt.xlabel('Year',fontsize=15,color='r')
plt.ylabel('Total profit',fontsize=15,color='r')
plt.show()

15.Year wise Total Revenue and Total profit findout.¶

In [297]:
df4=data.groupby(by=['Year'])['Total Profit'].sum().sort_values(ascending=False)
sns.set(rc={'figure.figsize':(20,5)})
df4.plot(kind='bar')
#ax=sns.barplot(x='Year',y='Total Profit',data = df4)
plt.title('Year wise Total Revenue and Total profit',fontsize=20,color='b')
plt.xlabel('Year',fontsize=15,color='r')
plt.ylabel('Total profit',fontsize=15,color='r')
plt.show()

16.Month-Year wise Total Revenue and Total profit findout.¶

In [231]:
df5=data.groupby(['Month-Year'])['Total Profit'].sum().sort_values(ascending=False)
sns.set(rc={'figure.figsize':(20,5)})
df5.plot(kind='bar')
#sns.barplot(x='MYear',y='Total Profit',data = Year,palette='viridis')

plt.title('Month-Year wise Total Revenue and Total profit',fontsize=20,color='b')
plt.xlabel('Year',fontsize=15,color='r')
plt.ylabel('Total profit',fontsize=15,color='r')
plt.show()

17.Item On Total cost and Profit.¶

In [7]:
da8=px.bar(data1,x='Region',y='Total Profit',color='Item Type',barmode='group',width=1000,height=500)
da8

18. Item Types wsie Total Profit and Total Cost¶

In [316]:
plt.figure(figsize=(7,5))
sns.scatterplot(x='Total Cost', y='Total Profit', data=data1, hue='Item Type');

19.Range of Unite price wsie item types¶

In [9]:
df10=sns.boxplot(x=data1['Unit Price'],y=data1['Item Type'])

# 20. All of dataset wise Pairplot¶

In [11]:
sns.set(rc={'figure.figsize':(20,5)})
sns.pairplot(data1)
Out[11]:
<seaborn.axisgrid.PairGrid at 0x17a53c6e390>
In [ ]: